Papers with English-to-Chinese
Cross-Lingual Transfer Learning for Speech Translation (2025.naacl-short)
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| Challenge: | Increasing interest in building multilingual foundation models for NLP and speech research has led to limited data collection for training ST systems. |
| Approach: | They propose to use Whisper to explore the behavior of multilingual speech foundation models with restricted data. |
| Outcome: | The proposed model can translate to Chinese with a single language, and it can perform transcriptions in other languages. |
English-to-Chinese Transliteration with Phonetic Auxiliary Task (2020.aacl-main)
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| Challenge: | Several NMT techniques have been used to enhance machine transliteration models, but few focus on the linguistic features specific to the relevant languages. |
| Approach: | They propose a phonetic auxiliary task that integrates phonetic features into a model to improve generalization performance of the main transliteration task. |
| Outcome: | The proposed model achieves similar performance to the current state of the art with a much smaller size. |
Opportunistic Decoding with Timely Correction for Simultaneous Translation (2020.acl-main)
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| Challenge: | Existing approaches to balancing translation quality and latency are either too aggressive or too conservative. |
| Approach: | They propose an opportunistic decoding technique that always (over-)generates a certain mount of extra words at each step to keep the audience on track with the latest information. |
| Outcome: | The proposed technique reduces latency and increases BLEU with no over-generating . it also corrects mistakes in the overgenerated words when observing more context . |
Attend, Translate and Summarize: An Efficient Method for Neural Cross-Lingual Summarization (2020.acl-main)
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| Challenge: | Existing methods for cross-lingual summarization are pipeline-based, but they suffer from error propagation. |
| Approach: | They propose a method that attends to some words in the source text, then translates them into the target language to get the final summary. |
| Outcome: | The proposed method outperforms baseline methods on Chinese-to-English and English-to Chinese summarization tasks. |
NCLS: Neural Cross-Lingual Summarization (D19-1)
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| Challenge: | Existing approaches to cross-lingual summarization divide the task into two steps: summarizing and translation. |
| Approach: | They propose to integrate two related tasks into the training process of CLS under multi-task learning to improve cross-lingual summarization. |
| Outcome: | The proposed framework improves on English-to-Chinese and Chinese-to English CLS human-corrected test sets. |
WeTS: A Benchmark for Translation Suggestion (2022.emnlp-main)
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| Challenge: | Existing studies focus on overall performance of machine translation but ignore TS performance, authors say . if TS is applied into post-editing, it will reduce the time and cost of post-production. |
| Approach: | They propose to use a golden corpus annotated by experts to generate a translation suggestion model. |
| Outcome: | The proposed model improves on the golden corpus annotated by translators on four translation directions. |